Publications by authors named "Michael Moseley"

Article Synopsis
  • The study aims to improve prediction of 90-day functional outcomes in ischemic stroke patients using a deep learning model that combines non-contrast CT images and clinical data, potentially aiding healthcare planning and clinical trials.
  • The dataset included 1,335 patients from multiple trials and registries, and the model demonstrated superior accuracy in predicting outcomes compared to models using only imaging or clinical data alone.
  • The fused model achieved a mean absolute error (MAE) of 0.94 for mRS score prediction and an AUC of 0.91 for identifying unfavorable outcomes, indicating it significantly outperforms existing methods.
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Article Synopsis
  • Artificial intelligence in medicine usually faces challenges related to small, non-diverse patient data due to privacy concerns, but federated learning (FL) offers a solution by allowing training across different hospitals without sharing sensitive data.
  • The newly developed FL-PedBrain platform is specifically designed for pediatric brain tumors, enabling collaborative training for tumor classification and segmentation across 19 international centers, addressing the lack of diverse datasets in this area.
  • FL-PedBrain shows impressive performance metrics, maintaining almost equivalent accuracy to centralized data training while significantly improving segmentation performance by 20 to 30% at external sites, and allows for the examination of data variability in real-world situations.
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Objective: Hydrocephalus is a challenging neurosurgical condition due to nonspecific symptoms and complex brain-fluid pressure dynamics. Typically, the assessment of hydrocephalus in children requires radiographic or invasive pressure monitoring. There is usually a qualitative focus on the ventricular spaces even though stress and shear forces extend across the brain.

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Background: Outlining acutely infarcted tissue on non-contrast CT is a challenging task for which human inter-reader agreement is limited. We explored two different methods for training a supervised deep learning algorithm: one that used a segmentation defined by majority vote among experts and another that trained randomly on separate individual expert segmentations.

Methods: The data set consisted of 260 non-contrast CT studies in 233 patients with acute ischemic stroke recruited from the multicenter DEFUSE 3 (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke 3) trial.

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Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of a wide range of neurological diseases. Positron emission tomography (PET) with radiolabeled water (O-water) is the gold-standard for the measurement of CBF in humans, however, it is not widely available due to its prohibitive costs and the use of short-lived radiopharmaceutical tracers that require onsite cyclotron production. Magnetic resonance imaging (MRI), in contrast, is more accessible and does not involve ionizing radiation.

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Cerebral blood flow (CBF) may be estimated from early-frame PET imaging of lipophilic tracers, such as amyloid agents, enabling measurement of this important biomarker in participants with dementia and memory decline. Although previous methods could map relative CBF, quantitative measurement in absolute units (mL/100 g/min) remained challenging and has not been evaluated against the gold standard method of [O]water PET. The purpose of this study was to develop and validate a minimally invasive quantitative CBF imaging method combining early [F]florbetaben (eFBB) with phase-contrast MRI using simultaneous PET/MRI.

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Background: Cerebrovascular reserve (CVR) reflects the capacity of cerebral blood flow (CBF) to change following a vasodilation challenge. Decreased CVR is associated with a higher stroke risk in patients with cerebrovascular diseases. While revascularization can improve CVR and reduce this risk in adult patients with vasculopathy such as those with Moyamoya disease, its impact on hemodynamics in pediatric patients remains to be elucidated.

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T2* relaxometry is one of the established methods to measure the effect of superparamagnetic iron oxide nanoparticles on tumor tissues with magnetic resonance imaging (MRI). Iron oxide nanoparticles shorten the T1, T2, and T2* relaxation times of tumors. While the T1 effect is variable based on the size and composition of the nanoparticles, the T2 and T2* effects are usually predominant, and T2* measurements are the most time-efficient in a clinical context.

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Objective: Repetitive head trauma is common in high-contact sports. Cerebral blood flow (CBF) can measure changes in brain perfusion that could indicate injury. Longitudinal studies with a control group are necessary to account for interindividual and developmental effects.

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Cerebral blood flow (CBF) is an important hemodynamic parameter to evaluate brain health. It can be obtained quantitatively using medical imaging modalities such as magnetic resonance imaging and positron emission tomography (PET). Although CBF in adults has been widely studied and linked with cerebrovascular and neurodegenerative diseases, CBF data in healthy children are sparse due to the challenges in pediatric neuroimaging.

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Article Synopsis
  • One-third of boys with X-linked adrenoleukodystrophy (ALD) develop brain lesions similar to those in multiple sclerosis, leading researchers to explore the impact of vitamin D on cerebral blood flow (CBF) in these patients.
  • The study, utilizing arterial spin labeling (ASL) to measure CBF at different time points, found a significant increase in vitamin D levels following supplementation but only mild, non-significant changes in overall CBF.
  • A positive correlation was noted between vitamin D levels and CBF specifically in the splenium, suggesting potential benefits, but further research is necessary to confirm causality.
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Objectives: Treatment and outcomes of acute stroke have been revolutionised by mechanical thrombectomy. Deep learning has shown great promise in diagnostics but applications in video and interventional radiology lag behind. We aimed to develop a model that takes as input digital subtraction angiography (DSA) videos and classifies the video according to (1) the presence of large vessel occlusion (LVO), (2) the location of the occlusion, and (3) the efficacy of reperfusion.

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Objectives: Iron oxide nanoparticles have been used to track the accumulation of chimeric antigen receptor (CAR) T cells with magnetic resonance imaging (MRI). However, the only nanoparticle available for clinical applications to date, ferumoxytol, has caused rare but severe anaphylactic reactions. MegaPro nanoparticles (MegaPro-NPs) provide an improved safety profile.

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Cerebrovascular reserve (CVR) reflects the capacity of cerebral blood flow (CBF) to change. Decreased CVR implies poor hemodynamics and is linked to a higher risk for stroke. Revascularization has been shown to improve CBF in patients with vasculopathy such as Moyamoya disease.

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Cerebrovascular reactivity (CVR) reflects the CBF change to meet different physiological demands. The reference CVR technique is PET imaging with vasodilators but is inaccessible to most patients. DSC can measure transit time to evaluate patients suspected of stroke, but the use of gadolinium may cause side-effects.

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Metastatic osteosarcoma has a poor prognosis with a 2-y, event-free survival rate of ∼15 to 20%, highlighting the need for the advancement of efficacious therapeutics. Chimeric antigen receptor (CAR) T-cell therapy is a potent strategy for eliminating tumors by harnessing the immune system. However, clinical trials with CAR T cells in solid tumors have encountered significant challenges and have not yet demonstrated convincing evidence of efficacy for a large number of patients.

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Automatic segmentation of lung nodules on computed tomography (CT) images is challenging owing to the variability of morphology, location, and intensity. In addition, few segmentation methods can capture intra-nodular heterogeneity to assist lung nodule diagnosis. In this study, we propose an end-to-end architecture to perform fully automated segmentation of multiple types of lung nodules and generate intra-nodular heterogeneity images for clinical use.

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Purpose: To investigate if a deep learning convolutional neural network (CNN) could enable low-dose fluorine 18 (F) fluorodeoxyglucose (FDG) PET/MRI for correct treatment response assessment of children and young adults with lymphoma.

Materials And Methods: In this secondary analysis of prospectively collected data (ClinicalTrials.gov identifier: NCT01542879), 20 patients with lymphoma (mean age, 16.

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Cerebrovascular reactivity (CVR), the capacity of the brain to increase cerebral blood flow (CBF) to meet changes in physiological demand, is an important biomarker to evaluate brain health. Typically, this brain "stress test" is performed by using a medical imaging modality to measure the CBF change between two states: at baseline and after vasodilation. However, since there are many imaging modalities and many ways to augment CBF, a wide range of CVR values have been reported.

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Gadolinium chelates have been used as standard contrast agents for clinical MRI for several decades. However, several investigators recently reported that rare Earth metals such as gadolinium are deposited in the brain for months or years. This is particularly concerning for children, whose developing brain is more vulnerable to exogenous toxins compared to adults.

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The prognosis for high-grade glioma (HGG) remains dismal and the extent of resection correlates with overall survival and progression free disease. Epidermal growth factor receptor (EGFR) is a biomarker heterogeneously expressed in HGG. We assessed the feasibility of detecting HGG using near-infrared fluorescent antibody targeting EGFR.

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Purpose: 3D multi-echo gradient-recalled echo (ME-GRE) can simultaneously generate time-of-flight magnetic resonance angiography (pTOF) in addition to T2*-based susceptibility-weighted images (SWI). We assessed the clinical performance of pTOF generated from a 3D ME-GRE acquisition compared with conventional TOF-MRA (cTOF).

Methods: Eighty consecutive children were retrospectively identified who obtained 3D ME-GRE alongside cTOF.

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Background Iron oxide nanoparticles are an alternative contrast agent for MRI. Gadolinium deposition has raised safety concerns, but it is unknown whether ferumoxytol administration also deposits in the brain. Purpose To investigate whether there are signal intensity changes in the brain at multiecho gradient imaging following ferumoxytol exposure in children and young adults.

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Background Whole-body diffusion-weighted (DW) MRI can help detect cancer with high sensitivity. However, the assessment of therapy response often requires information about tumor metabolism, which is measured with fluorine 18 fluorodeoxyglucose (FDG) PET. Purpose To compare tumor therapy response with whole-body DW MRI and FDG PET/MRI in children and young adults.

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Objective: To validate quantitative diffusion-weighted imaging (DWI) MRI thresholds that correlate with poor outcome in comatose cardiac arrest survivors, we conducted a clinician-blinded study and prospectively obtained MRIs from comatose patients after cardiac arrest.

Methods: Consecutive comatose post-cardiac arrest adult patients were prospectively enrolled. MRIs obtained within 7 days after arrest were evaluated.

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